载入数据
1Sys.setlocale('LC_ALL','C')
2load(file = "F:/Bioinfor_project/Breast/AS_research/AS/result/hubgene.Rdata")
3head(data)
4require(cowplot)
5require(tidyverse)
6require(ggplot2)
7require(ggsci)
8require(ggpubr)
9mydata<-data %>%
10 ## 基因表达数据gather,gather的范围应调整
11 gather(key="gene",value="Expression",CCL14:TUBB3) %>%
12 ##
13 dplyr::select(ID,gene,Expression,everything())
14head(mydata) ## 每个基因作为一个变量的宽数据
创建带有pvalue的箱线图
- 参考资料
- 展示绘图细节控制
1p <- ggboxplot(mydata, x = "group", y = "Expression",
2 color = "group", palette = "jama",
3 add = "jitter")
4# Add p-value
5p + stat_compare_means()
image.png
改变统计方法
1# Change method
2p + stat_compare_means(method = "t.test")
image.png
统计学意义标注
- label="p.signif"
- p.format等
- label.x标注位置
1p + stat_compare_means( label = "p.signif")
image.png
多组比较
- 给出global pvalue
1# Default method = "kruskal.test" for multiple groups
2ggboxplot(mydata, x = "gene", y = "Expression",
3 color = "gene",add="jitter", palette = "jama")+
4 stat_compare_means()
5
6# Change method to anova
7ggboxplot(mydata, x = "gene", y = "Expression",
8 color = "gene", add="jitter", palette = "jama")+
9 stat_compare_means(method = "anova")
image.png
image.png
指定比较
- 配对比较:会完成各个变量的比较，默认wilcox.test法，可修改
- my_comparisions:可以指定自己想要进行的比较
- 指定参考组，进行比较
1require(ggpubr)
2compare_means(Expression ~ gene, data = mydata)
3
4## 指定自己想要的比较
5# Visualize: Specify the comparisons you want
6my_comparisons <- list( c("CCL14", "HBA1"), c("HBA1", "CCL16"), c("CCL16", "TUBB3") )
7ggboxplot(mydata, x = "gene", y = "Expression",
8 color = "group",add = "jitter", palette = "jama")+
9 stat_compare_means(comparisons = my_comparisons)#+ # Add pairwise comparisons p-value
10 #stat_compare_means() # Add global p-value
image.png
指定参考组
指定CCL14作为参考组与其它各组比较
ref.group
1compare_means(Expression ~ gene, data = mydata, ref.group = "CCL14",
2 method = "t.test")
3# Visualize
4mydata %>%
5 filter(group=="TNBC") %>% # 筛选TNBC数据
6ggboxplot( x = "gene", y = "Expression",
7 color = "gene",add = "jitter", palette = "nejm")+
8 stat_compare_means(method = "anova")+ # Add global p-value
9 stat_compare_means(label = "p.signif", method = "t.test",
10 ref.group = "CCL14")
image.png
多基因分面
按另外一个变量分组比较
1## 比较各个基因在TNBC与Normal表达
2compare_means( Expression ~ group, data = mydata,
3 group.by = "gene")
4# Box plot facetted by "gene"
5p <- ggboxplot(mydata, x = "group", y = "Expression",
6 color = "group", palette = "jco",
7 add = "jitter",
8 facet.by = "gene", short.panel.labs = FALSE)
9# Use only p.format as label. Remove method name.
10p + stat_compare_means(label = "p.format")
image.png
将pvalue换成星号
- hide.ns = TRUE.参数可隐藏ns
1p + stat_compare_means(label = "p.signif", label.x = 1.5)
image.png
将各个图绘制在一张图中
1p <- ggboxplot(mydata, x = "gene", y = "Expression",
2 color = "group", palette = "nejm",
3 add = "jitter")
4p + stat_compare_means(aes(group = group))
image.png
修改下pvalue展示的方式
1# Show only p-value
2p + stat_compare_means(aes(group = group), label = "p.format")
image.png
用星号表示pvalue
1# Use significance symbol as label
2p + stat_compare_means(aes(group = group), label = "p.signif")
image.png
配对样本比较
要求x,y具有相同的样本数，进行一一配对比较
1head(ToothGrowth)
2compare_means(len ~ supp, data = ToothGrowth,
3 group.by = "dose", paired = TRUE)
4# Box plot facetted by "dose"
5p <- ggpaired(ToothGrowth, x = "supp", y = "len",
6 color = "supp", palette = "jama",
7 line.color = "gray", line.size = 0.4,
8 facet.by = "dose", short.panel.labs = FALSE)
9# Use only p.format as label. Remove method name.
10p + stat_compare_means(label = "p.format", paired = TRUE)
image.png
封装为函数命名为group_box
- 功能：已经选定的基因绘制箱线图
- - 参数1：group分组变量，可以是自己所有感兴趣的变量
- 参数2：mydata为整理好的清洁数据,gene为长数据(gather版本)
1head(mydata)
2group_box<-function(group=group,data=mydata){
3 p <- ggboxplot(mydata, x = "gene", y = "Expression",
4 color = group,
5 palette = "nejm",
6 add = "jitter")
7p + stat_compare_means(aes(group = group))
8}
9
10##
11group_box(group="PAM50",data = mydata)
封装为函数命名为group_box
- 功能：已经选定的基因绘制箱线图
- - 参数1：group分组变量，可以是自己所有感兴趣的变量
- 参数2：mydata为整理好的清洁数据,gene为长数据(gather版本)
1head(mydata)
2group_box<-function(group=group,data=mydata){
3 p <- ggboxplot(mydata, x = "gene", y = "Expression",
4 color = group,
5 palette = "nejm",
6 add = "jitter")
7p + stat_compare_means(aes(group = group))
8}
9
10##
11group_box(group="PAM50",data = mydata)
image.png
封装函数gene_box
- 目的功能：对感兴趣的基因绘制和分组绘制boxplot
- 注意这时使用的应该是基因的宽数据，因为涉及到单个基因作为变量
1head(data)
2usedata<-data
3## 封装函数
4gene_box<-function(gene="CCL14",group="group",data=usedata){
5p <- ggboxplot(data, x = group, y = gene,
6 ylab = sprintf("Expression of %s",gene),
7 xlab = group,
8 color = group,
9 palette = "nejm",
10 add = "jitter")
11p + stat_compare_means(aes(group = group))
12}
1gene_box(gene="CCL14")
image.png
牛刀小试
1gene_box(gene="CCL16",group="PAM50")
image.png
批量绘制
- 目的功能：绘制任意基因，任意分组，批量绘制一气呵成了
- 封装函数+lapply批量绘制无敌
- 在lapply中的函数参数设置，不在原函数中，而是直接放置在lapply中
- do.call中参数1为函数，+c()包含原函数的参数设置，同样参数设置不在原函数中
1require(gridExtra)
2head(data)
3
4## 需要批量绘制的基因名
5name<-colnames(data)[3:6]
6## 批量绘图
7p<-lapply(name,gene_box,group = "T_stage")
8## 组图
9do.call(grid.arrange,c(p,ncol=2))
image.png
